1 Firing rate of IL neurons during glycinergic fiber activation

In these experiments glycinergic fibers were photactivated while the firing rate of individual IL neurons was recorded. Some of the IL cells had very low baseline activity. In order to increase the firing rate of the IL neurons and to detect the effect of the glycinergic fiber activation a small tail pinch was applied. The firing rate of the recorded IL cells decreased during the glycinergic fiber activation.

1.1 Loading data

Loading AP and stimuli times from stimulus and from baseline data to IL_stim_firing and to IL_baseline_firing tables:

IL_stim_firing <- CreateRecTibble(AP_times = read_csv(file.path("data", 
    "IL_MFR", "stimulus", "AP_times.csv")), 
    stim_times = read_csv(file.path("data", 
        "IL_MFR", "stimulus", "stim_times.csv")))
IL_stim_firing %>% head()
IL_baseline_firing <- CreateRecTibble(
  AP_times = read_csv(file.path("data", "IL_MFR", "baseline", "AP_times.csv")),
  stim_times = read_csv(file.path("data", "IL_MFR", "baseline", "stim_times.csv"))
)
IL_stim_firing %>% head()



1.2 Summary information

Summary information of the stimulus and baseline recording files:

  • Variables:
    • file names (file_name)
    • number of channels in the raw recording files (No_ch)
    • number of APs (No_AP_unit, No_AP_unit2)
    • number of stimulus trains (No_Stim)
    • LFP sampling rate (samp_rate_lfp)
    • unit sampling rate (samp_rate_unit)
    • length of the recording (rec_length)
    • length of the stimulus trains (No_trains)
    • Are the lengths of the stimulus trains equal in the recording (train_length_equal)
    • starting time of the stimulus trains (train_start)
    • ending time of the stimulus trains (train_end)



CELL_INFO table to store the cell categories. It was created manually using the information from the summary excel table (Glicy_juxta-fm_exp_records_injection_sum.xls)

  • Variables
    • cell identification (cell_id). To find the recording file use the summary excel table
    • file names (file_name)
    • spontaneously active cells (bl_activity)
    • induced firing (pinch)
    • individually identified neurons (ident)
    • control cells (control)



1.3 Calculations

1.3.1 Firing rates

b_d_a_MFR: Calculating the number of APs -using a custom made function (BDACalculator)- before during and after the stimulus trains (b_d_a_MFR).

b_d_a_MFR <- lapply(CELL_INFO$cell_id, BDACalculator, data = IL_stim_firing) %>%
  bind_rows() %>%
  dplyr::mutate(FR = No_AP / train_length) %>%
  dplyr::group_by(stim_cond, cell_id) %>%
  dplyr::summarise(MFR = mean(FR))


sd_mean_isi: Calculating the baseline MFR of the recorded IL cells from the IL_baseline_firing table using a custom made function (SDMeanISI). The results are stored in the sd_mean_isi table.


1.3.2 Ranks (strength of inhibition)

cellranks: Calculating ranks based on the activity change from “baseline” to “during stimulus”. If the activity change is negative (decreased MFR) the asigned rank is negative, if it is positive (increased MFR) the assigned rank is positive.


Calculating the firing rate change during stimulus (photoactivation of the glycinergic fibers) compared to baseline:




\[\mathbf{activity\_change} = \frac{during\_MFR - base\_MFR}{base\_MFR} * 100\]

cellranks <- b_d_a_MFR %>%
  dplyr::group_by(stim_cond) %>%
  dplyr::mutate(base_MFR = sd_mean_isi$MFR) %>%
  dplyr::mutate(activity_change = ((MFR - base_MFR) / base_MFR * 100) %>% round(2)) %>%
  dplyr::filter(stim_cond == "d") %>%
  dplyr::mutate(change_rank = ifelse(activity_change > 0,
    rank(activity_change),
    -rank(-activity_change)
  )) %>%
  ungroup() %>%
  left_join(CELL_INFO %>%
    select(cell_id, control, pinch, position),
  by = "cell_id"
  )
cellranks

cellranks_before_stim: Calculating ranks based on the activity change from “before stimulus” to “during stimulus”. If the activity change is negative (decreased MFR) the asigned rank is negative, if it is positive (increased MFR) the assigned rank is positive.


Calculating the firing rate change during stimulus (photoactivation of the glycinergic fibers) compared to before stimulus:




\[\mathbf{activity\_change} = \frac{during\_MFR - before\_MFR}{before\_MFR} * 100\]

cellranks_before_stim <- b_d_a_MFR %>%
  spread(key = stim_cond, value = MFR) %>%
  dplyr::mutate(activity_change = ((d - b) / b * 100) %>% round(2)) %>%
  dplyr::mutate(change_rank = ifelse(activity_change > 0,
    rank(activity_change),
    -rank(-activity_change)
  )) %>%
  left_join(CELL_INFO %>%
    select(cell_id, control, pinch, position),
  by = "cell_id"
  )
cellranks_before_stim



1.3.3 Data to plot

TO_PLOT: Combining b_d_a_MFR (firing rate of 29 IL neurons b/d/a stim) with sd_mean_isi table (baseline firing rate of the same 29 neurons), joining with CELL_INFO containing important information of the cells (baseline activity, identified, pinched, control) and with cellranks containing the ranks asigned to each cells based on the changes in MFR during the stimulus compared to baseline.

TO_PLOT <- bind_rows(
  sd_mean_isi %>%
    select(MFR, cell_id, stim_cond),
  b_d_a_MFR
) %>%
  left_join(CELL_INFO %>%
    select(-file_name),
  by = "cell_id"
  ) %>%
  left_join(cellranks %>%
    select(cell_id, change_rank, activity_change),
  by = "cell_id"
  )

datatable(TO_PLOT,
  caption = "TO_PLOT table",
  rownames = TRUE,
  options = list(pageLength = 50, scrollX = T, scrollY = "500px", dom = "t")
)



1.4 Plotting

1.4.1 Baseline vs. “before” stimulus activity

Comparison of baseline and “before” stimulus firing rates in the case of spontaneously active and sponteneously inactive (pinch) neurons. Spontaneously inactive neurons showed significantly higher MFR before stiulus compared to baseline.



1.4.2 MFR before, during and after stimulus



1.4.3 Strength of inhibition

Firing rate change during stimulus (photoactivation of the glycinergic fibers) compared to baseline:



Firing rate change during stimulus (photoactivation of the glycinergic fibers) compared to before stimulus:



Plotting the change in MFR from “baseline” to “during stimulus”. Coloring based on the strength of the inhibition (rank)













2 Activity of PRF glycinergic cells during PFC photoactivation

2.1 Loading and transforming (hidden code) data

source(file.path("supplementary_functions", "CreateRecTibble.R"))
RECORDINGS <- CreateRecTibble(
  AP_times = read_csv(file.path("data", "cortical_stim", "AP_times.csv")),
  stim_times = read_csv(file.path("data", "cortical_stim", "stim_times.csv"))
)

STIM_RESULTS <- read_csv(file.path("output_data", "cortical_stim_analysis.csv"))
  • List of tibbles used to store data:
    • RECORDINGS tibble: stores AP and stim time stamps, number of stimuli in each train, stimulus frequency categories (eg. 8 10 and 12 Hz belong to 10 Hz category)
    • STIM_RESULTS: stores the data for PSTHs (CAREFUL WITH THIS!! It’s generated by the “CreatePSTHTibble” function but the frequencies have to be changed manually, and it can create duplicates! I have to work on that! If something doesn’t look right on PSTHs or response probability plots, check this one first)
    • hist_stats: stores PSTH statistics calculated from ggplot
    • RESP_PROB: response probability and latency data calculated from stim results and recordings
    • APC: number of APs after each stimulus in a 50 ms window

2.2 Plotting

2.2.1 Distribution of frist APs after cortical activation

2.2.2 PSTH @ 1 Hz cortical activation

2.2.3 PSTH @ 10 Hz cortical activation

2.2.4 PSTH @ 20 Hz cortical activation

2.2.5 Response probability and latency

2.2.6 Number of APs after each stimulus in stimulus trains (50 ms window)

3 Spontaneous desynchronization of the FC slow oscillation

3.1 Loading data

Loading mat files to file_list object

Content of mat files:

  • AP channel
    • title: channel name
    • comment: notes about the channel
    • resolution: 2.5e-06
    • interval: time between two data points in sec (5e-05)
    • scale: scaling factor (y scale), real = (data * scale) + offset
    • offset: scale offset
    • units: unit of the channel (volt)
    • length: number of APs
    • items: No. points easch AP is represented by
    • trigger:
    • traces:
    • times: times of the APs (first data point of the waveform)
    • codes: marker codes of the APs
    • values: matrix containing the waveforms of the APs (described by a given No. points)
  • EEG channel
    • title: channel name
    • comment:
    • interval:
    • scale: scaling factor (y scale), real = (data * scale) + offset
    • offset: scale offset
    • units:
    • start:
    • length:
    • values:
file_to_load <- file_list[[1]]
filename <- as.character(substring(file_to_load, 1, nchar(file_to_load) - 4))
raw.rec <- readMat(file.path("data", file_to_load))

### takes the first AP (first row) and tells the index of point with the max value
points_to_peak <- which(raw.rec$ap[, , 1]$values[1, ] ==
  max(raw.rec$ap[, , 1]$values[1, ])) %>%
  as.numeric()

### time of the peak of the APs after its first point
raw.rec$ap[, , 1]$interval * points_to_peak
        [,1]
[1,] 0.00055
ap <- raw.rec$ap[, , 1]$times %>% as.double()
ap_peaks <- tibble(peak_times = (ap + c(raw.rec$ap[, , 1]$interval * points_to_peak)))

——- insert code here ——–

(spont_desynchron_analysis.R), 7 recordings

4 Terminals

4.1 Loading data

file_path <- file.path("data", 
    "Emi_terminals", "terminal_measures.csv")
TERMINALS <- read.table(file = file_path, 
    sep = ";", header = T, na.string = "na")
head(TERMINALS)

4.2 Bouton area on different dendritic domains

4.3 Area of boutons with m2 and unknown origin

---
title: "Frontal cortex-driven glycinergic inhibition of the intralaminar thalamus"
author: "Viktor Plattner, Emilia Bosz, Laszlo Acsady"
output:
  html_document:
    number_sections: yes
    df_print: paged
    toc: yes
    toc_float: yes
  pdf_document:
    number_sections: yes
    toc: yes
    keep_tex: true
  html_notebook:
    number_sections: yes
    toc: yes
    toc_float: yes
spacing: single
fontsize: 12pt
always_allow_html: yes
---




<style type="text/css">

body{ /* Normal  */
      font-size: 14px;
      font-family: "Arial", Helvetica, sans-serif;
  }
td {  /* Table  */
  font-size: 12px;
}
h1.title {
  font-size: 38px;
  font-family: "Arial", Helvetica, sans-serif;
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code.r{ /* Code block */
    font-size: 12px;
}
pre { /* Code block - determines code spacing between lines */
    font-size: 14px;
}
</style>



```{r setup, include=FALSE}
library(R.matlab)
library(tidyverse)
library(reshape2)
library(ggrepel)
library(DT)
library(ggpubr)
library(styler)
library(formatR)

library(bspec) ### power spectrum
library(WaveletComp) ### wavelet
library(diptest) ### to test distribution uni/multimodality (ISI)
library(exactRankTests) ### wilcox.test for ties
library(WaveletComp) ### wavelet
library(signal)

library(STAR)
library(see) # for half violinplot
library(gghalves) # for half violinplot

source(file.path("supplementary_functions", "CreateRecTibble.R"))

knitr::opts_chunk$set(tidy = TRUE, tidy.opts = list(width.cutoff = 30))
```


<br><br>



# Firing rate of IL neurons during glycinergic fiber activation

In these experiments glycinergic fibers were photactivated while the firing rate of individual IL neurons was recorded. Some of the IL cells had very low baseline activity. In order to increase the firing rate of the IL neurons and to detect the effect of the glycinergic fiber activation a small tail pinch was applied. The firing rate of the recorded IL cells decreased during the glycinergic fiber activation. 


## Loading data


Loading AP and stimuli times from *stimulus* and from *baseline* data to **IL_stim_firing** and to **IL_baseline_firing** tables:

```{r, message=FALSE, warning=FALSE, tidy=TRUE}
IL_stim_firing <- CreateRecTibble(
  AP_times = read_csv(file.path("data", "IL_MFR", "stimulus", "AP_times.csv")),
  stim_times = read_csv(file.path("data", "IL_MFR", "stimulus", "stim_times.csv"))
)
IL_stim_firing %>% head()
```

```{r, message=FALSE, warning=FALSE}
IL_baseline_firing <- CreateRecTibble(
  AP_times = read_csv(file.path("data", "IL_MFR", "baseline", "AP_times.csv")),
  stim_times = read_csv(file.path("data", "IL_MFR", "baseline", "stim_times.csv"))
)
IL_stim_firing %>% head()
```

<br><br>

## Summary information 

Summary information of the *stimulus* and  *baseline* recording files:

* Variables:
  + file names (*file_name*)
  + number of channels in the raw recording files (*No_ch*)
  + number of APs (*No_AP_unit*, *No_AP_unit2*)
  + number of stimulus trains (*No_Stim*)
  + LFP sampling rate (*samp_rate_lfp*)
  + unit sampling rate (*samp_rate_unit*)
  + length of the recording (*rec_length*)
  + length of the stimulus trains (*No_trains*)
  + Are the lengths of the stimulus trains equal in the recording (*train_length_equal*)
  + starting time of the stimulus trains (*train_start*)
  + ending time of the stimulus trains (*train_end*)

```{r, message=FALSE,echo=FALSE }

info_stim <- read_csv(file.path("data", "IL_MFR", "stimulus", "file_info.csv"))
datatable(info_stim,
  caption = "Summary table of the stimulus recording files (info_stim)",
  rownames = TRUE,
  options = list(pageLength = 50, scrollX = T, scrollY = "500px", dom = "t")
)
```

<br>

```{r,message=FALSE,echo=FALSE}
info_baseline <- read_csv(file.path("data", "IL_MFR", "baseline", "file_info.csv"))
datatable(info_baseline,
  caption = "Summary table of the baseline recording files (info_baseline)",
  rownames = TRUE,
  options = list(pageLength = 50, scrollX = T, scrollY = "500px", dom = "t")
)
```
<br>

**CELL_INFO** table to store the cell categories. It was created manually using the information from the summary excel table (*Glicy_juxta-fm_exp_records_injection_sum.xls*)

* Variables
  + cell identification (*cell_id*). To find the recording file use the summary excel table
  + file names (*file_name*)
  + spontaneously active cells (*bl_activity*)
  + induced firing (*pinch*)
  + individually identified neurons (*ident*)
  + control cells (*control*)


```{r, echo=FALSE}
### CELL INFO to store categories (spontaneously active, induced firing, identified)
CELL_INFO <- info_baseline %>%
  select(file_name) %>%
  add_column(cell_id = substr(info_baseline$file_name, 1, 6), .before = "file_name") %>%
  dplyr::mutate(bl_activity = T) %>%
  dplyr::mutate(bl_activity = replace(
    bl_activity,
    .$cell_id == "cell05" |
      .$cell_id == "cell10" |
      .$cell_id == "cell12" |
      .$cell_id == "cell13" |
      .$cell_id == "cell14" |
      .$cell_id == "cell15" |
      .$cell_id == "cell21" |
      .$cell_id == "cell24",
    F
  )) %>%
  dplyr::mutate(pinch = T) %>%
  dplyr::mutate(pinch = replace(
    pinch,
    .$cell_id == "cell01" |
      .$cell_id == "cell06" |
      .$cell_id == "cell07" |
      .$cell_id == "cell08" |
      .$cell_id == "cell18" |
      .$cell_id == "cell19" |
      .$cell_id == "cell20" |
      .$cell_id == "cell21" |
      .$cell_id == "cell23" |
      .$cell_id == "cell24" |
      .$cell_id == "cell25" |
      .$cell_id == "cell26" |
      .$cell_id == "cell27" |
      .$cell_id == "cell28",
    F
  )) %>%
  dplyr::mutate(ident = T) %>%
  dplyr::mutate(ident = replace(
    ident,
    .$cell_id == "cell06" |
      .$cell_id == "cell07" |
      .$cell_id == "cell11" |
      .$cell_id == "cell12" |
      .$cell_id == "cell13" |
      .$cell_id == "cell14" |
      .$cell_id == "cell21" |
      .$cell_id == "cell22" |
      .$cell_id == "cell23" |
      .$cell_id == "cell24" |
      .$cell_id == "cell25" |
      .$cell_id == "cell26" |
      .$cell_id == "cell27",
    F
  )) %>%
  dplyr::mutate(control = F) %>%
  dplyr::mutate(control = replace(
    control,
    .$cell_id == "cell25" |
      .$cell_id == "cell26" |
      .$cell_id == "cell27" |
      .$cell_id == "cell28" |
      .$cell_id == "cell29",
    T
  )) %>%
  dplyr::mutate(position = "unknown") %>%
  dplyr::mutate(position = replace(
    position,
    .$cell_id == "cell01" |
      .$cell_id == "cell02" |
      .$cell_id == "cell03" |
      .$cell_id == "cell10" |
      .$cell_id == "cell11" |
      .$cell_id == "cell14" |
      .$cell_id == "cell15",
    "IL"
  )) %>%
  dplyr::mutate(position = replace(
    position,
    .$cell_id == "cell20" |
      .$cell_id == "cell21" |
      .$cell_id == "cell22",
    "transient"
  )) %>%
  dplyr::mutate(position = replace(
    position,
    .$cell_id == "cell08" |
      .$cell_id == "cell16" |
      .$cell_id == "cell17" |
      .$cell_id == "cell23" |
      .$cell_id == "cell24",
    "PF"
  ))

datatable(CELL_INFO,
  caption = "CELL_INFO table",
  rownames = TRUE,
  options = list(pageLength = 50, scrollX = T, scrollY = "500px", dom = "t")
)

# IL_stim_firing$file_name %>% unique() %>% length()
# info_stim$file_name %>% length()
```


<br><br>

## Calculations
### Firing rates

```{r, include=FALSE}
### CALCULATING --------

IL_stim_firing <- left_join(IL_stim_firing,
  info_stim %>%
    select(
      file_name,
      rec_length,
      train_starts,
      train_ends,
      train_length
    ),
  by = "file_name"
)

### calculating stim start times
start_times <- IL_stim_firing %>%
  dplyr::group_by(file_name) %>%
  dplyr::summarise(train_starts = unique(train_starts), train_ends = unique(train_ends)) %>%
  dplyr::mutate(cell_id = substr(info_stim$file_name, 1, 6)) %>%
  dplyr::group_by(cell_id) %>%
  dplyr::summarise(train_starts = unique(train_starts)) %>%
  pull(train_starts) %>%
  strsplit(",") %>%
  set_names(substr(info_stim$file_name, 1, 6)) %>%
  lapply(as.numeric)

### calculating stim end times
end_times <- IL_stim_firing %>%
  dplyr::group_by(file_name) %>%
  dplyr::summarise(train_starts = unique(train_starts), train_ends = unique(train_ends)) %>%
  dplyr::mutate(cell_id = substr(info_stim$file_name, 1, 6)) %>%
  dplyr::group_by(cell_id) %>%
  dplyr::summarise(train_ends = unique(train_ends)) %>%
  pull(train_ends) %>%
  strsplit(",") %>%
  set_names(substr(info_stim$file_name, 1, 6)) %>%
  lapply(as.numeric)


cell_list <- substr(info_stim$file_name, 1, 6)
CELL_INFO$cell_id
```

```{r, include=FALSE}
### function to calculate the number of AP b/d/a stim. Arguments: data (IL_stim_firing table with rec_length and stimulus train information)
BDACalculator <- function(data, list) {
  cell_list <- list

  train_length <- data %>%
    dplyr::filter(substr(file_name, 1, 6) == cell_list) %>%
    select(train_length) %>%
    pull() %>%
    `[[`(1)



  if (
    (data %>%
      dplyr::filter(substr(file_name, 1, 6) == cell_list, signal_type == "AP") %>%
      select(unit_id) %>%
      unique() %>%
      pull() %>%
      length()) == 1
  ) {
    ### In case of one unit in the file:
    AP_times <- data %>%
      dplyr::filter(substr(file_name, 1, 6) == cell_list, signal_type == "AP") %>%
      select(signal_time) %>%
      pull()

    AP_numbers <- matrix(0, nrow = length(start_times[[cell_list]]), ncol = 3)
    colnames(AP_numbers) <- c("b", "d", "a")

    for (train_num in 1:length(start_times[[cell_list]])) {
      ### before
      AP_numbers[train_num, 1] <- length(AP_times[start_times[[cell_list]][train_num] - train_length
      < AP_times & AP_times < start_times[[cell_list]][train_num]])

      ### during
      AP_numbers[train_num, 2] <- length(AP_times[AP_times > start_times[[cell_list]][train_num] & AP_times < end_times[[cell_list]][train_num]])

      ### after
      AP_numbers[train_num, 3] <- length(AP_times[AP_times > end_times[[cell_list]][train_num] &
        AP_times < end_times[[cell_list]][train_num] + train_length])
    }

    melt(AP_numbers, varnames = c("train", "stim_cond"), value.name = "No_AP") %>%
      as.tibble() %>%
      add_column(cell_id = cell_list) %>%
      add_column(train_length = train_length)
  } else {
    ### in case of multiple units in the file:
    AP_times_1 <- data %>%
      dplyr::filter(substr(file_name, 1, 6) == cell_list, signal_type == "AP", unit_id == 1) %>%
      select(signal_time) %>%
      pull()

    AP_numbers_1 <- matrix(0, nrow = length(start_times[[cell_list]]), ncol = 3)
    colnames(AP_numbers_1) <- c("b", "d", "a")

    for (train_num in 1:length(start_times[[cell_list]])) {
      ### before
      AP_numbers_1[train_num, 1] <- length(AP_times_1[start_times[[cell_list]][train_num] - train_length
      < AP_times_1 & AP_times_1 < start_times[[cell_list]][train_num]])

      ### during
      AP_numbers_1[train_num, 2] <- length(AP_times_1[AP_times_1 > start_times[[cell_list]][train_num] &
        AP_times_1 < end_times[[cell_list]][train_num]])

      ### after
      AP_numbers_1[train_num, 3] <- length(AP_times_1[AP_times_1 > end_times[[cell_list]][train_num] &
        AP_times_1 < end_times[[cell_list]][train_num] + train_length])
    }


    AP_times_2 <- data %>%
      dplyr::filter(substr(file_name, 1, 6) == cell_list, signal_type == "AP", unit_id == 2) %>%
      select(signal_time) %>%
      pull()

    AP_numbers_2 <- matrix(0, nrow = length(start_times[[cell_list]]), ncol = 3)
    colnames(AP_numbers_2) <- c("b", "d", "a")

    for (train_num in 1:length(start_times[[cell_list]])) {
      ### before
      AP_numbers_2[train_num, 1] <- length(AP_times_2[start_times[[cell_list]][train_num] - train_length
      < AP_times_2 & AP_times_2 < start_times[[cell_list]][train_num]])

      ### during
      AP_numbers_2[train_num, 2] <- length(AP_times_2[AP_times_2 > start_times[[cell_list]][train_num] &
        AP_times_2 < end_times[[cell_list]][train_num]])

      ### after
      AP_numbers_2[train_num, 3] <- length(AP_times_2[AP_times_2 > end_times[[cell_list]][train_num] &
        AP_times_2 < end_times[[cell_list]][train_num] + train_length])
    }



    if (cell_list %>% substr(5, 6) %>% as.numeric() %>% nchar() == 1) {
      new_name <- paste0(
        "cell0",
        cell_list %>% substr(5, 6) %>% as.numeric() + 1
      )
    }

    if (cell_list %>% substr(5, 6) %>% as.numeric() %>% nchar() == 2) {
      new_name <- paste0(
        "cell",
        cell_list %>% substr(5, 6) %>% as.numeric() + 1
      )
    }

    bind_rows(
      melt(AP_numbers_1, varnames = c("train", "stim_cond"), value.name = "No_AP") %>%
        as.tibble() %>%
        add_column(cell_id = cell_list) %>%
        add_column(train_length = train_length),

      melt(AP_numbers_2, varnames = c("train", "stim_cond"), value.name = "No_AP") %>%
        as.tibble() %>%
        add_column(cell_id = new_name) %>%
        add_column(train_length = train_length)
    )
  } # else
} # foo
```

**b_d_a_MFR**: Calculating the number of APs -using a custom made function (*BDACalculator*)- before during and after the stimulus trains (*b_d_a_MFR*). 

```{r}
b_d_a_MFR <- lapply(CELL_INFO$cell_id, BDACalculator, data = IL_stim_firing) %>%
  bind_rows() %>%
  dplyr::mutate(FR = No_AP / train_length) %>%
  dplyr::group_by(stim_cond, cell_id) %>%
  dplyr::summarise(MFR = mean(FR))
```

<br>
**sd_mean_isi**: Calculating the baseline MFR of the recorded IL cells from the *IL_baseline_firing* table using a custom made function (*SDMeanISI*). The results are stored in the *sd_mean_isi* table.

```{r, include=FALSE}
IL_baseline_firing <- left_join(IL_baseline_firing,
  info_baseline %>%
    select(file_name, rec_length),
  by = "file_name"
) %>%
  dplyr::mutate(stim_cond = "baseline")

### function to calculate ISI mean and SD during baseline activity of the cells
SDMeanISI <- function(f_name) {


  # mean_isi <- IL_baseline_firing %>%
  #   dplyr::filter(file_name == f_name, signal_type == "AP") %>%
  #   select(signal_time) %>%
  #   pull() %>%
  #   diff() %>%
  #   mean() * 1000 %>%
  #   `names<-`(f_name)
  #
  # sd_isi <- IL_baseline_firing %>%
  #   dplyr::filter(file_name == f_name, signal_type == "AP") %>%
  #   select(signal_time) %>%
  #   pull() %>%
  #   diff() %>%
  #   sd() * 1000 %>%
  #   `names<-`(f_name)
  #
  # return(list(MFR = mean_isi,
  #             SDFR = sd_isi))

  tmp2 <- IL_baseline_firing %>%
    dplyr::filter(file_name == f_name, signal_type == "AP") %>%
    select(signal_time) %>%
    pull() %>%
    diff() %>%
    mean() * 1000 %>%
      tibble(mean_isi = .)
  tmp2 %>%
    dplyr::mutate(sd_isi = IL_baseline_firing %>%
      dplyr::filter(file_name == f_name, signal_type == "AP") %>%
      select(signal_time) %>%
      pull() %>%
      diff() %>%
      sd() * 1000) %>%
    dplyr::mutate(file_name = f_name)
}

sd_mean_isi <- lapply(info_baseline$file_name, SDMeanISI) %>%
  bind_rows() %>%
  as.tibble()

sd_mean_isi <- sd_mean_isi %>%
  dplyr::mutate(cell_id = substr(sd_mean_isi$file_name, 1, 6)) %>%
  dplyr::mutate(MFR = 1000 / mean_isi) %>%
  dplyr::mutate(SD_FR = 1000 / sd_isi) %>%
  dplyr::mutate(MFR_inc = MFR + SD_FR) %>%
  dplyr::mutate(MFR_dec = MFR - SD_FR) %>%
  # dplyr::mutate(MFR_inc = 1000/(mean_isi-sd_isi)) %>%
  # dplyr::mutate(MFR_dec = 1000/(mean_isi+sd_isi)) %>%
  dplyr::mutate(stim_cond = "baseline") %>%
  dplyr::mutate(MFR_dec = replace(MFR_dec, sd_mean_isi$MFR_dec < 0, 0))
```
<br>

### Ranks (strength of inhibition)
**cellranks**: Calculating ranks based on the activity change from "*baseline*" to "*during stimulus*". If the activity change is negative (decreased MFR) the asigned rank is negative, if it is positive (increased MFR) the assigned rank is positive.

<br>

Calculating the firing rate change during stimulus (photoactivation of the glycinergic fibers) compared to baseline:

<br><br><br>

$$\mathbf{activity\_change} = \frac{during\_MFR - base\_MFR}{base\_MFR} * 100$$
<br>

```{r}
cellranks <- b_d_a_MFR %>%
  dplyr::group_by(stim_cond) %>%
  dplyr::mutate(base_MFR = sd_mean_isi$MFR) %>%
  dplyr::mutate(activity_change = ((MFR - base_MFR) / base_MFR * 100) %>% round(2)) %>%
  dplyr::filter(stim_cond == "d") %>%
  dplyr::mutate(change_rank = ifelse(activity_change > 0,
    rank(activity_change),
    -rank(-activity_change)
  )) %>%
  ungroup() %>%
  left_join(CELL_INFO %>%
    select(cell_id, control, pinch, position),
  by = "cell_id"
  )
cellranks
```



**cellranks_before_stim**: Calculating ranks based on the activity change from "*before stimulus*" to "*during stimulus*". If the activity change is negative (decreased MFR) the asigned rank is negative, if it is positive (increased MFR) the assigned rank is positive.

<br>

Calculating the firing rate change during stimulus (photoactivation of the glycinergic fibers) compared to before stimulus:

<br><br><br>

$$\mathbf{activity\_change} = \frac{during\_MFR - before\_MFR}{before\_MFR} * 100$$
<br>



```{r}
cellranks_before_stim <- b_d_a_MFR %>%
  spread(key = stim_cond, value = MFR) %>%
  dplyr::mutate(activity_change = ((d - b) / b * 100) %>% round(2)) %>%
  dplyr::mutate(change_rank = ifelse(activity_change > 0,
    rank(activity_change),
    -rank(-activity_change)
  )) %>%
  left_join(CELL_INFO %>%
    select(cell_id, control, pinch, position),
  by = "cell_id"
  )
cellranks_before_stim
```



<br><br>

### Data to plot
**TO_PLOT**: Combining *b_d_a_MFR* (firing rate of 29 IL neurons b/d/a stim) with *sd_mean_isi* table (baseline firing rate of the same 29 neurons), joining with CELL_INFO containing important information of the cells (baseline activity, identified, pinched, control) and with *cellranks* containing the ranks asigned to each cells based on the changes in MFR during the stimulus compared to baseline.

```{r, message=FALSE, warning=FALSE}
TO_PLOT <- bind_rows(
  sd_mean_isi %>%
    select(MFR, cell_id, stim_cond),
  b_d_a_MFR
) %>%
  left_join(CELL_INFO %>%
    select(-file_name),
  by = "cell_id"
  ) %>%
  left_join(cellranks %>%
    select(cell_id, change_rank, activity_change),
  by = "cell_id"
  )

datatable(TO_PLOT,
  caption = "TO_PLOT table",
  rownames = TRUE,
  options = list(pageLength = 50, scrollX = T, scrollY = "500px", dom = "t")
)
```
<br><br>

## Plotting

### Baseline vs. "before" stimulus activity

Comparison of baseline and "before" stimulus firing rates in the case of spontaneously active and sponteneously inactive (pinch) neurons. Spontaneously inactive neurons showed significantly higher MFR before stiulus compared to baseline.

```{r, fig.width=10, echo = FALSE, warning=FALSE}
ggplot(
  data = TO_PLOT %>%
    dplyr::filter(
      control == F,
      stim_cond == "baseline" | stim_cond == "b"
    ),
  mapping = aes(
    x = forcats::fct_relevel(stim_cond, "baseline", "b"),
    y = MFR
  )
) +
  geom_boxplot(width = 0.2, alpha = 0.5) +
  geom_point(aes(fill = pinch),
    shape = 21,
    # fill = "#EB8104",
    # color = "white",
    size = 4
  ) +
  # stroke = 2) +
  geom_line(
    aes(group = cell_id, col = pinch)
  ) +
  theme(panel.background = element_rect(fill = 0)) +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 20),
    text = element_text(size = 20)
  ) +
  xlab("Recording period") +
  facet_wrap(vars(pinch)) +
  stat_compare_means(method = "wilcox.test", paired = T) +
  geom_label_repel(
    data = TO_PLOT %>%
      dplyr::filter(
        control == F,
        stim_cond == "baseline"
      ),
    mapping = aes(label = cell_id),
    nudge_x = -.9,
    direction = "y",
    # hjust = 2,
    segment.size = .2
  )
```

<br><br>

### MFR before, during and after stimulus 

```{r, fig.width=14, fig.height=12 ,echo=FALSE, warning=FALSE}
ggplot(
  data = TO_PLOT %>%
    dplyr::filter(
      control == F,
      stim_cond == "b" | stim_cond == "d" | stim_cond == "a"
    ),
  mapping = aes(
    x = forcats::fct_relevel(stim_cond, "b", "d", "a"),
    y = MFR
  )
) +
  theme(panel.background = element_rect(fill = 0)) +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 20),
    text = element_text(size = 20)
  ) +
  ggtitle("Pinch", ) +
  xlab("Recording period") +
  ylim(0, 35) +
  geom_boxplot(width = 0.2, alpha = 0.5) +
  geom_point(
    shape = 21,
    fill = "#EB8104",
    # color = "white",
    size = 4
  ) +
  # stroke = 2) +
  geom_line(
    aes(group = cell_id),
    color = "#EB8104"
  ) +
  geom_label_repel(
    data = TO_PLOT %>%
      dplyr::filter(
        control == F,
        stim_cond == "a"
      ),
    mapping = aes(label = cell_id),
    nudge_x = 0.15,
    direction = "y",
    hjust = -0.5,
    segment.size = .2
  ) +
  facet_wrap(vars(pinch))
```
<br><br>

### Strength of inhibition

Firing rate change **during** stimulus (photoactivation of the glycinergic fibers) compared to **baseline**:

```{r, fig.width=10, echo=FALSE,warning=FALSE}
ggplot(
  data = cellranks,
  mapping = aes(x = change_rank, y = activity_change)
) +
  geom_point(data = cellranks %>%
    dplyr::filter(control == F), size = 3) +
  geom_point(data = cellranks %>%
    dplyr::filter(control == T), size = 3, col = "magenta") +
  ### label for negative values
  geom_text_repel(
    data = cellranks %>%
      dplyr::filter(activity_change < 0),
    aes(label = cell_id),
    direction = "y",
    angle = 90,
    hjust = .5,
    nudge_y = -2
  ) +

  ### label for positive values
  geom_text_repel(
    data = cellranks %>%
      dplyr::filter(activity_change > 0),
    aes(label = cell_id),
    direction = "x",
    angle = 0,
    hjust = .5,
    nudge_x = -1
  ) +
  geom_hline(yintercept = 0, col = "grey", lty = "dashed") +
  annotate("text", x = 27, y = -17, label = "Baseline activity", col = "gray") +
  geom_vline(xintercept = 0, col = "grey", lty = "dashed") +
  annotate("text", x = -2, y = 280, label = "Negative rank: FR decrease", col = "gray", angle = 90) +
  annotate("text", x = 2, y = 280, label = "Positive rank: FR increase", col = "gray", angle = 90) +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 20),
    text = element_text(size = 20)
  ) +
  ylim(-max(cellranks$activity_change), max(cellranks$activity_change))
```

<br><br>

Firing rate change **during** stimulus (photoactivation of the glycinergic fibers) compared to **before** stimulus:

```{r, fig.width=10, echo=FALSE,warning=FALSE}

ggplot(
  data = cellranks_before_stim,
  mapping = aes(x = change_rank, y = activity_change)
) +
  geom_point(
    data = cellranks_before_stim %>%
      dplyr::filter(control == F), size = 6,
    aes(
      col = position,
      shape = pinch
    )
  ) +
  scale_color_manual(values = c("IL" = "green", "transient" = "blue", "PF" = "red", "unknown" = "black", "control" = "magenta")) +
  scale_shape_manual(values = c("TRUE" = "+", "FALSE" = "-")) +
  geom_point(data = cellranks_before_stim %>%
    dplyr::filter(control == T), size = 3, col = "magenta") +
  ### label for negative values
  geom_text_repel(
    data = cellranks_before_stim %>%
      dplyr::filter(activity_change < 0),
    aes(label = cell_id),
    direction = "y",
    angle = 90,
    hjust = .5,
    nudge_y = -2
  ) +

  ### label for positive values
  geom_text_repel(
    data = cellranks_before_stim %>%
      dplyr::filter(activity_change > 0),
    aes(label = cell_id),
    direction = "x",
    angle = 0,
    hjust = .5,
    nudge_x = -1
  ) +
  geom_hline(yintercept = 0, col = "grey", lty = "dashed") +
  annotate("text", x = 27, y = -17, label = "Baseline activity", col = "gray") +
  geom_vline(xintercept = 0, col = "grey", lty = "dashed") +
  annotate("text", x = -2, y = 280, label = "Negative rank: FR decrease", col = "gray", angle = 90) +
  annotate("text", x = 2, y = 280, label = "Positive rank: FR increase", col = "gray", angle = 90) +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 20),
    text = element_text(size = 20)
  ) +
  xlab("Ranks") +
  ylab("Change in firing rate [%]") +
  ylim(-max(cellranks_before_stim$activity_change), max(cellranks_before_stim$activity_change))
```












<br><br>

Plotting the change in MFR from "*baseline*" to "*during stimulus*". Coloring based on the strength of the inhibition (*rank*)

```{r, fig.width=10, fig.height=12, echo=FALSE,warning=FALSE}
ggplot(
  data = TO_PLOT %>%
    dplyr::filter(
      control == F,
      stim_cond == "baseline" | stim_cond == "d"
    ),
  mapping = aes(
    x = forcats::fct_relevel(stim_cond, "baseline", "d", "a"),
    y = MFR
  )
) +
  geom_boxplot(width = 0.2, alpha = 0.5) +
  geom_point(aes(fill = change_rank),
    shape = 21,
    # fill = "#EB8104",
    # color = "white",
    size = 4
  ) +
  # stroke = 2) +
  geom_line(
    aes(group = cell_id, col = change_rank)
  ) +
  geom_label_repel(
    data = TO_PLOT %>%
      dplyr::filter(
        control == F,
        stim_cond == "baseline"
      ),
    mapping = aes(label = cell_id, col = change_rank),
    # nudge_x = -.1,
    direction = "y",
    hjust = 4,
    segment.size = .2
  ) +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 20),
    text = element_text(size = 20)
  ) +
  xlab("Recording period") +
  labs(fill = "Rank", col = "Rank")
```


<br><br><br><br><br><br><br><br><br><br><br><br>


# Activity of PRF glycinergic cells during PFC photoactivation

## Loading and transforming (hidden code) data


```{r loading data, message=FALSE, warning=FALSE}
source(file.path("supplementary_functions", "CreateRecTibble.R"))
RECORDINGS <- CreateRecTibble(
  AP_times = read_csv(file.path("data", "cortical_stim", "AP_times.csv")),
  stim_times = read_csv(file.path("data", "cortical_stim", "stim_times.csv"))
)


### CAREFUL WITH THIS!! It's generated by the "CreatePSTHTibble" function but the frequencies have to be changed manually, and it can create duplicates! I have to work on that!
STIM_RESULTS <- read_csv(file.path("output_data", "cortical_stim_analysis.csv"))
```


```{r transforming data, echo=FALSE, message=FALSE, warning=FALSE}
### adding stimulus number within train
RECORDINGS <- RECORDINGS %>%
  dplyr::mutate(stim_number = 0)

### calculating stimulus number within train
initial_value <- RECORDINGS$stim_freq[1] %>%
  `comment<-`("First value of stim_freq variable. When it changes stimulus counting restarts")

stim_counter <- 1 %>%
  `comment<-`("Counts stimuli in a train")
index <- 1 %>%
  `comment<-`("Tracks the position (index) of stim_freq")

RECORDINGS$stim_number[1] <- stim_counter
repeat{
  if (RECORDINGS$stim_freq[index + 1] == initial_value) {
    RECORDINGS$stim_number[index + 1] <- stim_counter + 1
    stim_counter <- stim_counter + 1
    index <- index + 1
  } else {
    initial_value <- RECORDINGS$stim_freq[index + 1]
    stim_counter <- 1
    RECORDINGS$stim_number[index + 1] <- stim_counter
    index <- index + 1
  }

  if (index == length(RECORDINGS$stim_number)) {
    break
  }
}

### replacing stim_number with NA at "AP"
RECORDINGS$stim_number[RECORDINGS$signal_type == "AP"] <- NA

### categorizing stimuli
RECORDINGS <- RECORDINGS %>%
  dplyr::mutate(stim_freq_categ = stim_freq) %>%
  dplyr::mutate(stim_freq_categ = replace(
    x = stim_freq_categ,
    list = (stim_freq_categ == 12 | stim_freq_categ == 8),
    values = 10
  )) %>%
  dplyr::mutate(stim_freq_categ = replace(
    x = stim_freq_categ,
    list = (stim_freq_categ == 18),
    values = 20
  ))
```

```{r calculating hist stats, echo=FALSE, message=FALSE, warning=FALSE}
source(file.path("supplementary_functions", "HistStats.R"))

STIM_RESULTS %>%
  dplyr::group_by(freq) %>%
  dplyr::summarise(n_times = length(unique(animal_id))) %>%
  ungroup() %>%
  as.matrix() %>%
  unname() -> rep_matrix

### set PSTH range
PSTH_range <- c(-0.01, 0.04)


hist_stats <- STIM_RESULTS %>%
  dplyr::filter(
    first_ap_reltimes > PSTH_range[1],
    first_ap_reltimes < PSTH_range[2]
  ) %>%
  dplyr::group_by(freq) %>%
  do(gp_hist = ggplot(
    data = .,
    mapping = aes(x = first_ap_reltimes)
  ) +
    xlim(PSTH_range[1], PSTH_range[2]) +
    geom_histogram(aes(fill = animal_id), binwidth = 0.001) +
    scale_fill_brewer(
      palette = "Set3",
      name = "Animal"
      # guide = FALSE
    ) +
    facet_wrap(~animal_id, ncol = 3) +
    geom_vline(xintercept = 0) +
    xlab("Time (s)") +
    ylab("Count") +
    ggtitle(paste0(.$freq, " Hz"))
  ) %>%
  `[[`(2) %>%
  lapply(. %>% HistStats() %>% as.tibble()) %>%
  bind_rows() %>%
  add_column(
    freq = c(
      rep(rep_matrix[1, 1], rep_matrix[1, 2]),
      rep(rep_matrix[2, 1], rep_matrix[2, 2]),
      rep(rep_matrix[3, 1], rep_matrix[3, 2])
    ),
    .after = "animal_id"
  )
```


```{r calculating response prob, echo=FALSE, message=FALSE, warning=FALSE}
RESP_PROB <- left_join(
  STIM_RESULTS %>%
    dplyr::filter(
      first_ap_reltimes > PSTH_range[1],
      first_ap_reltimes < PSTH_range[2]
    ) %>%
    dplyr::group_by(animal_id, freq) %>%
    dplyr::summarise(No_APs = length(first_ap_reltimes)) %>%
    dplyr::rename(stim_freq = freq) %>%
    dplyr::mutate(stim_freq = replace(stim_freq, stim_freq == 18, 20)) %>%
    dplyr::mutate(stim_freq = replace(stim_freq, stim_freq == 8, 10)),

  RECORDINGS %>%
    dplyr::filter(signal_type == "stim", stim_freq_categ %in% c("1", "10", "20")) %>%
    dplyr::group_by(animal_id, stim_freq_categ) %>%
    dplyr::summarise(No_stim = length(signal_time)) %>%
    dplyr::mutate(stim_freq = as.numeric(stim_freq_categ)) %>%
    select(-stim_freq_categ)
) %>%
  add_column(No_APs_range = hist_stats %>%
    arrange(animal_id) %>%
    dplyr::mutate(freq = replace(freq, freq == 18, 20)) %>%
    dplyr::mutate(freq = replace(freq, freq == 8, 10)) %>%
    dplyr::rename(stim_freq = freq) %>% pull(range_count_sum)) %>%
  dplyr::mutate(resp_prob = (No_APs / No_stim) * 100) %>%
  dplyr::mutate(resp_prob_range = (No_APs_range / No_stim) * 100) %>%
  add_column(peak_latency = hist_stats %>%
    arrange(animal_id) %>%
    pull(peak)) %>%
  add_column(mean_latency = hist_stats %>%
    arrange(animal_id) %>%
    pull(mean))
```

```{r calculating AP count, echo=FALSE, message=FALSE, warning=FALSE}
APCount <- function(animal) {
  tmp_ap <- RECORDINGS %>%
    dplyr::filter(signal_type == "AP", animal_id == animal) %>%
    select(signal_time) %>%
    pull()

  stim_trains <- RECORDINGS %>%
    dplyr::filter(signal_type == "stim") %>%
    dplyr::mutate(
      lag_sfc = lag(stim_freq),
      tmp_var = ifelse(lag_sfc == stim_freq & !is.na(lag_sfc), 0, 1),
      cum_tmp_var = cumsum(tmp_var)
    ) %>%
    dplyr::group_by(animal_id, stim_freq, cum_tmp_var) %>%
    dplyr::mutate(train_number = group_indices()) %>%
    ungroup() %>%
    select(-lag_sfc, -tmp_var, -cum_tmp_var) %>%
    dplyr::filter(animal_id == animal) %>%
    dplyr::group_by(stim_freq_categ, train_number, stim_number) %>%
    dplyr::summarise(
      No_APs = length(tmp_ap[tmp_ap > signal_time & tmp_ap < signal_time + 0.05]),
      AP_times = paste(tmp_ap[tmp_ap > signal_time & tmp_ap < signal_time + 0.05], collapse = ","),
      range_lower = signal_time,
      range_upper = signal_time + 0.05
    ) %>%
    add_column(animal_id = animal, .before = 1) %>%
    ungroup()
  return(stim_trains)
}

APC <- lapply(RECORDINGS$animal_id %>% unique(), APCount) %>% bind_rows()
```


* List of tibbles used to store data:
  + RECORDINGS tibble: stores AP and stim time stamps, number of stimuli in each train, stimulus frequency categories (eg. 8 10 and 12 Hz belong to 10 Hz category)
  + STIM_RESULTS: stores the data for PSTHs (CAREFUL WITH THIS!! It's generated by the "CreatePSTHTibble" function but the frequencies have to be changed manually, and it can create duplicates! I have to work on that! If something doesn't look right on PSTHs or response probability plots, check this one first)
  + hist_stats: stores PSTH statistics calculated from ggplot 
  + RESP_PROB: response probability and latency data calculated from stim results and recordings
  + APC: number of APs after each stimulus in a 50 ms window

```{r echo=FALSE, message=FALSE, warning=FALSE, tidy=TRUE}
datatable(RECORDINGS,
  caption = "RECORDINGS tibble",
  rownames = TRUE,
  options = list(pageLength = 50, scrollX = T, scrollY = "500px", dom = "t")
)
```

```{r echo=FALSE, message=FALSE, warning=FALSE, tidy=TRUE}
datatable(STIM_RESULTS,
  caption = "STIM_RESULTS tibble",
  rownames = TRUE,
  options = list(pageLength = 50, scrollX = T, scrollY = "500px", dom = "t")
)
```

```{r echo=FALSE, message=FALSE, warning=FALSE, tidy=TRUE}
datatable(hist_stats,
  caption = "PSTH statistics (hist_stats tibble)",
  rownames = TRUE,
  options = list(pageLength = 50, scrollX = T, scrollY = "500px", dom = "t")
)
```

```{r echo=FALSE, message=FALSE, warning=FALSE}
datatable(RESP_PROB,
  caption = "Response probabilities",
  rownames = TRUE,
  options = list(pageLength = 50, scrollX = T, scrollY = "500px", dom = "t")
)
```

```{r echo=FALSE, message=FALSE, warning=FALSE}
datatable(APC,
  caption = "Number of APs folowing each stimulus",
  rownames = TRUE,
  options = list(pageLength = 50, scrollX = T, scrollY = "500px", dom = "t")
)
```


## Plotting

### Distribution of frist APs after cortical activation


```{r echo=FALSE, fig.width=10, message=FALSE, warning=FALSE}

source(file.path("supplementary_functions", "geom_flat_violin.R"))
# PSTH_range <- c(0, 0.05)
ggplot(
  data = STIM_RESULTS %>%
    dplyr::filter(
      first_ap_reltimes > PSTH_range[1],
      first_ap_reltimes < PSTH_range[2]
    ),
  mapping = aes(y = first_ap_reltimes, x = animal_id)
) +
  coord_flip() +
  gghalves::geom_half_violin(side = "r", trim = T, scale = "width") +
  # geom_dotplot(binaxis = "y",
  #              dotsize = 4,
  #              stackdir = "down",
  #              binwidth = 0.0002,method = "histodot",binpositions = "all",
  #              ) +
  # see::geom_violindot(binwidth = 0.0001,size_dots = 6,) +
  geom_jitter(position = position_jitter(0), shape = "|") +
  # geom_boxplot(width = .1, outlier.colour = NA, position = "dodge") +
  geom_hline(yintercept = 0) +
  facet_wrap(~freq)
```

### PSTH @ 1 Hz cortical activation

```{r echo=FALSE, fig.width=10, fig.height=10, message=FALSE, warning=FALSE}
### main plot

### set PSTH range: see earlier (calculating hist stats chunk)

### select frequency to plot (1, 8, 18)
freq_to_plot <- 1 %>% as.character()

gp_hist <- ggplot(
  data = STIM_RESULTS %>%
    dplyr::filter(
      first_ap_reltimes > PSTH_range[1],
      first_ap_reltimes < PSTH_range[2],
      freq == freq_to_plot
      # animal_id == "GII_21"
    ),
  mapping = aes(x = first_ap_reltimes)
) +
  xlim(PSTH_range[1], PSTH_range[2]) +
  geom_histogram(aes(fill = animal_id), binwidth = 0.001) +
  scale_fill_brewer(
    palette = "Set3",
    name = "Animal"
    # guide = FALSE
  ) +
  facet_wrap(~animal_id, ncol = 3) +
  geom_vline(xintercept = 0) +
  xlab("Time (s)") +
  ylab("Count")



### lines
gp_hist +
  ### median
  geom_segment(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(
      x = median,
      xend = median,
      y = y_max + 7,
      yend = y_max + 18
    ),
    size = 1
  ) +

  ### data range
  geom_segment(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(
      x = quantile25 - 1.5 * IQR,
      xend = quantile25,
      y = y_max + 12.5,
      yend = y_max + 12.5
    )
  ) +

  geom_segment(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(x = quantile75, xend = quantile75 + 1.5 * IQR, y = y_max + 12.5, yend = y_max + 12.5)
  ) +

  ### IQR (box)
  geom_rect(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(xmin = quantile25, xmax = quantile75, ymin = y_max + 7, ymax = y_max + 18),
    color = "red",
    alpha = 0,
    inherit.aes = F
  ) +

  ### mean
  geom_point(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(x = mean, y = y_max + 4), size = 2
  ) +

  ### peak
  geom_point(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(x = peak, y = y_max + 4),
    shape = 25,
    size = 2,
    fill = "red"
  )
```

### PSTH @ 10 Hz cortical activation

```{r echo=FALSE, fig.width=10, fig.height=10, message=FALSE, warning=FALSE}
### main plot

### set PSTH range: see earlier (calculating hist stats chunk)

### select frequency to plot (1, 8, 18)
freq_to_plot <- 8 %>% as.character()

gp_hist <- ggplot(
  data = STIM_RESULTS %>%
    dplyr::filter(
      first_ap_reltimes > PSTH_range[1],
      first_ap_reltimes < PSTH_range[2],
      freq == freq_to_plot
      # animal_id == "GII_21"
    ),
  mapping = aes(x = first_ap_reltimes)
) +
  xlim(PSTH_range[1], PSTH_range[2]) +
  geom_histogram(aes(fill = animal_id), binwidth = 0.001) +
  scale_fill_brewer(
    palette = "Set3",
    name = "Animal"
    # guide = FALSE
  ) +
  facet_wrap(~animal_id, ncol = 3) +
  geom_vline(xintercept = 0) +
  xlab("Time (s)") +
  ylab("Count")



### lines
gp_hist +
  ### median
  geom_segment(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(
      x = median,
      xend = median,
      y = y_max + 7,
      yend = y_max + 18
    ),
    size = 1
  ) +

  ### data range
  geom_segment(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(
      x = quantile25 - 1.5 * IQR,
      xend = quantile25,
      y = y_max + 12.5,
      yend = y_max + 12.5
    )
  ) +

  geom_segment(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(x = quantile75, xend = quantile75 + 1.5 * IQR, y = y_max + 12.5, yend = y_max + 12.5)
  ) +

  ### IQR (box)
  geom_rect(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(xmin = quantile25, xmax = quantile75, ymin = y_max + 7, ymax = y_max + 18),
    color = "red",
    alpha = 0,
    inherit.aes = F
  ) +

  ### mean
  geom_point(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(x = mean, y = y_max + 4), size = 2
  ) +

  ### peak
  geom_point(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(x = peak, y = y_max + 4),
    shape = 25,
    size = 2,
    fill = "red"
  )
```

### PSTH @ 20 Hz cortical activation

```{r echo=FALSE, fig.width=10, fig.height=8, message=FALSE, warning=FALSE}
### main plot

### set PSTH range: see earlier (calculating hist stats chunk)

### select frequency to plot (1, 8, 18)
freq_to_plot <- 18 %>% as.character()

gp_hist <- ggplot(
  data = STIM_RESULTS %>%
    dplyr::filter(
      first_ap_reltimes > PSTH_range[1],
      first_ap_reltimes < PSTH_range[2],
      freq == freq_to_plot
      # animal_id == "GII_21"
    ),
  mapping = aes(x = first_ap_reltimes)
) +
  xlim(PSTH_range[1], PSTH_range[2]) +
  geom_histogram(aes(fill = animal_id), binwidth = 0.001) +
  scale_fill_brewer(
    palette = "Set3",
    name = "Animal"
    # guide = FALSE
  ) +
  facet_wrap(~animal_id, ncol = 3) +
  geom_vline(xintercept = 0) +
  xlab("Time (s)") +
  ylab("Count")



### lines
gp_hist +
  ### median
  geom_segment(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(
      x = median,
      xend = median,
      y = y_max + 7,
      yend = y_max + 18
    ),
    size = 1
  ) +

  ### data range
  geom_segment(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(
      x = quantile25 - 1.5 * IQR,
      xend = quantile25,
      y = y_max + 12.5,
      yend = y_max + 12.5
    )
  ) +

  geom_segment(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(x = quantile75, xend = quantile75 + 1.5 * IQR, y = y_max + 12.5, yend = y_max + 12.5)
  ) +

  ### IQR (box)
  geom_rect(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(xmin = quantile25, xmax = quantile75, ymin = y_max + 7, ymax = y_max + 18),
    color = "red",
    alpha = 0,
    inherit.aes = F
  ) +

  ### mean
  geom_point(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(x = mean, y = y_max + 4), size = 2
  ) +

  ### peak
  geom_point(
    data = hist_stats %>%
      dplyr::filter(freq == freq_to_plot) %>%
      dplyr::group_by(animal_id),
    mapping = aes(x = peak, y = y_max + 4),
    shape = 25,
    size = 2,
    fill = "red"
  )
```


### Response probability and latency



```{r echo=FALSE, fig.width=10, message=FALSE, warning=FALSE}
ggplot(
  data = RESP_PROB %>%
    dplyr::filter(animal_id != "GII_20"),
  mapping = aes(
    x = as.factor(stim_freq),
    y = resp_prob_range
  )
) +
  geom_point(
    aes(fill = animal_id),
    color = "black",
    size = 3,
    shape = 21,
    position = position_jitter(0.1)
  ) +
  # geom_line(aes(group = animal_id)) +
  geom_boxplot(alpha = 0, width = 0.2, lwd = 1, fatten = 1.2) +
  xlab("Stimulus frequency") +
  ylab("Response probability") +
  geom_signif(
    comparisons = list(c("1", "10")),
    map_signif_level = TRUE,
    test = "mood.test"
  ) +
  geom_signif(
    comparisons = list(c("10", "20")),
    map_signif_level = TRUE,
    test = "mood.test"
  )
```


```{r echo=FALSE, fig.width=10, message=FALSE, warning=FALSE}
ggplot(
  data = RESP_PROB %>%
    gather(
      key = "latency_type",
      value = "latency_value",
      peak_latency,
      mean_latency
    ) %>%
    dplyr::filter(animal_id != "GII_20"),
  mapping = aes(
    x = as.factor(stim_freq),
    y = latency_value * 1000
  )
) +
  ### data visualization layers
  geom_point(
    aes(color = latency_type),
    position = position_dodge(0.4),
    size = 3
  ) +
  geom_boxplot(
    aes(color = latency_type),
    position = position_dodge(0.4),
    width = 0.3,
    alpha = 0,
    lwd = 1,
    fatten = 1.2
  ) +
  ylim(0, 25) +
  xlab("Stimulus frequency") +
  ylab("Peak latency") +
  scale_color_discrete(
    name = "Latency",
    breaks = c("mean_latency", "peak_latency"),
    labels = c("Mean", "Peak")
  )
```


### Number of APs after each stimulus in stimulus trains (50 ms window)


```{r echo=FALSE, fig.width=10, message=FALSE, warning=FALSE}
ggplot(
  data = APC %>%
    dplyr::filter(stim_freq_categ %in% c("1", "10", "20"), stim_number < 11) %>%
    dplyr::group_by(animal_id, stim_freq_categ, stim_number) %>%
    dplyr::summarise(No_APs = sum(No_APs)) %>%
    ungroup() %>%
    dplyr::group_by(stim_freq_categ, stim_number) %>%
    dplyr::summarise(
      No_AP_mean = mean(No_APs),
      No_AP_sd = sd(No_APs),
      SEM = sd(No_APs) / sqrt(length(No_APs))
    ),
  mapping = aes(
    x = stim_number,
    y = No_AP_mean,
    colour = as.factor(stim_freq_categ),
    group = as.factor(stim_freq_categ)
  )
) +
  geom_point() +
  geom_line() +
  xlab("No. stimulus in train") +
  ylab("Mean no. APs accross animals") +
  labs(color = "Frequency") +
  ylim(0, 18) +
  scale_x_discrete(limits = as.character(c(1:10)))
```

```{r echo=FALSE, fig.width=10, fig.height=4, message=FALSE, warning=FALSE}
ggplot(
  data = APC %>%
    dplyr::filter(stim_freq_categ %in% c("1", "10", "20"), stim_number < 11) %>%
    dplyr::group_by(animal_id, stim_freq_categ, stim_number) %>%
    dplyr::summarise(No_APs = sum(No_APs)),
  mapping = aes(
    x = stim_number,
    y = No_APs,
    color = as.factor(animal_id),
    linetype = as.factor(animal_id)
  )
) +
  geom_point() +
  geom_line() +
  scale_x_discrete(limits = c(1:10), breaks = c(1:10)) +
  geom_label_repel(
    data = . %>%
      dplyr::filter(stim_number == 10),
    aes(label = animal_id),
    direction = "y",
    hjust = -2
  ) +
  expand_limits(x = 12) +

  # xlim(1,10) +
  facet_wrap(~stim_freq_categ) +
  labs(linetype = "Animals", color = "Animals") +
  xlab("No. stimulus in train") +
  ylab("Number of APs")
```



# Spontaneous desynchronization of the FC slow oscillation 

## Loading data 

Loading mat files to *file_list* object

```{r,  echo=FALSE,warning=FALSE,message=FALSE}
file_list <- list.files(
  path = "data",
  pattern = "*.mat", full.names = F, recursive = F
)

```

Content of mat files:

* AP channel
  + **title:** channel name
  + **comment:** notes about the channel
  + **resolution:** 2.5e-06
  + **interval:** time between two data points in sec (5e-05)
  + **scale:** scaling factor (y scale), real = (data * scale) + offset
  + **offset:** scale offset
  + **units:** unit of the channel (volt)
  + **length:** number of APs
  + **items:** No. points easch AP is represented by
  + **trigger:**
  + **traces:**
  + **times:** times of the APs (first data point of the waveform)
  + **codes:** marker codes of the APs
  + **values:** matrix containing the waveforms of the APs (described by a given No. points)
 
* EEG channel
  + **title:** channel name
  + **comment:**
  + **interval:**
  + **scale:** scaling factor (y scale), real = (data * scale) + offset
  + **offset:** scale offset
  + **units:**
  + **start:**
  + **length:**
  + **values:**

```{r}
file_to_load <- file_list[[1]]
filename <- as.character(substring(file_to_load, 1, nchar(file_to_load) - 4))
raw.rec <- readMat(file.path("data", file_to_load))

### takes the first AP (first row) and tells the index of point with the max value
points_to_peak <- which(raw.rec$ap[, , 1]$values[1, ] ==
  max(raw.rec$ap[, , 1]$values[1, ])) %>%
  as.numeric()

### time of the peak of the APs after its first point
raw.rec$ap[, , 1]$interval * points_to_peak

ap <- raw.rec$ap[, , 1]$times %>% as.double()
ap_peaks <- tibble(peak_times = (ap + c(raw.rec$ap[, , 1]$interval * points_to_peak)))
```


------- insert code here --------
 
(spont_desynchron_analysis.R), 7 recordings

# Terminals

## Loading data

```{r message=FALSE, warning=FALSE, tidy=TRUE}
file_path <- file.path(
  "data",
  "Emi_terminals",
  "terminal_measures.csv"
)
TERMINALS <- read.table(file = file_path, sep = ";", header = T, na.string = "na")
head(TERMINALS)
```

## Bouton area on different dendritic domains

```{r echo=FALSE, fig.width=10, message=FALSE, warning=FALSE}
ggplot(data = TERMINALS, mapping = aes(x = postion, y = b_area)) +
  geom_half_violin(
    position = position_nudge(x = -.15),
    trim = F,
    side = "l",
    scale = "area",
    draw_quantiles = c(0.5),
    aes(fill = postion)
  ) +
  geom_jitter(position = position_jitter(0.1))
```

## Area of boutons with *m2* and *unknown* origin

```{r fig.width=10, echo=FALSE, warning=FALSE, message=FALSE}
ggplot(
  data = TERMINALS,
  mapping = aes(x = origin, y = b_area)
) +
  geom_half_violin(
    position = position_nudge(x = -.15),
    trim = F,
    side = "l",
    scale = "count",
    draw_quantiles = c(0.5)
    # fill = alpha(0.1),
    # aes(col = postion)
  ) +
  geom_jitter(
    position = position_jitter(0.1),
    aes(col = postion)
  ) +
  annotate("text",
    x = 0.7,
    y = 1,
    label = paste(
      "n = ",
      TERMINALS %>%
        dplyr::group_by(origin) %>%
        dplyr::summarise(n = length(b_area)) %>%
        dplyr::filter(origin == "m2") %>%
        select(n) %>%
        as.character()
    )
  ) +
  annotate("text",
    x = 1.6,
    y = 1,
    label = paste(
      "n = ",
      TERMINALS %>%
        dplyr::group_by(origin) %>%
        dplyr::summarise(n = length(b_area)) %>%
        dplyr::filter(origin == "unknown") %>%
        select(n) %>%
        as.character()
    )
  )
```
